Overview

Dataset statistics

Number of variables11
Number of observations123
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.7 KiB
Average record size in memory89.0 B

Variable types

Numeric11

Alerts

Happiness is highly correlated with GDP and 2 other fieldsHigh correlation
GDP is highly correlated with Happiness and 2 other fieldsHigh correlation
SocialSupport is highly correlated with Happiness and 3 other fieldsHigh correlation
Health is highly correlated with Happiness and 2 other fieldsHigh correlation
Freedom is highly correlated with PositiveAffectHigh correlation
PositiveAffect is highly correlated with FreedomHigh correlation
NegativeAffect is highly correlated with SocialSupportHigh correlation
Happiness is highly correlated with GDP and 2 other fieldsHigh correlation
GDP is highly correlated with Happiness and 2 other fieldsHigh correlation
SocialSupport is highly correlated with Happiness and 3 other fieldsHigh correlation
Health is highly correlated with Happiness and 2 other fieldsHigh correlation
Freedom is highly correlated with PositiveAffectHigh correlation
PositiveAffect is highly correlated with FreedomHigh correlation
NegativeAffect is highly correlated with SocialSupportHigh correlation
Happiness is highly correlated with GDP and 2 other fieldsHigh correlation
GDP is highly correlated with Happiness and 2 other fieldsHigh correlation
SocialSupport is highly correlated with Happiness and 1 other fieldsHigh correlation
Health is highly correlated with Happiness and 1 other fieldsHigh correlation
Happiness is highly correlated with GDP and 5 other fieldsHigh correlation
GDP is highly correlated with Happiness and 3 other fieldsHigh correlation
SocialSupport is highly correlated with Happiness and 3 other fieldsHigh correlation
Health is highly correlated with Happiness and 4 other fieldsHigh correlation
Freedom is highly correlated with Generosity and 1 other fieldsHigh correlation
Generosity is highly correlated with Happiness and 1 other fieldsHigh correlation
PositiveAffect is highly correlated with Happiness and 1 other fieldsHigh correlation
NegativeAffect is highly correlated with Happiness and 2 other fieldsHigh correlation
ConfidenceInGovernment is highly correlated with GDP and 1 other fieldsHigh correlation
df_index has unique values Unique
Happiness has unique values Unique
SocialSupport has unique values Unique
Freedom has unique values Unique
PositiveAffect has unique values Unique
NegativeAffect has unique values Unique

Reproduction

Analysis started2022-05-21 10:26:32.986047
Analysis finished2022-05-21 10:26:52.205301
Duration19.22 seconds
Software versionpandas-profiling v3.1.1
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct123
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.05691057
Minimum2
Maximum147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-05-21T12:26:52.366207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile9.1
Q138.5
median75
Q3110.5
95-th percentile139.9
Maximum147
Range145
Interquartile range (IQR)72

Descriptive statistics

Standard deviation42.5042526
Coefficient of variation (CV)0.56629366
Kurtosis-1.184800183
Mean75.05691057
Median Absolute Deviation (MAD)36
Skewness-0.007406709994
Sum9232
Variance1806.611489
MonotonicityStrictly increasing
2022-05-21T12:26:52.492387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21
 
0.8%
1111
 
0.8%
1091
 
0.8%
1081
 
0.8%
1071
 
0.8%
1061
 
0.8%
1051
 
0.8%
1041
 
0.8%
1031
 
0.8%
1021
 
0.8%
Other values (113)113
91.9%
ValueCountFrequency (%)
21
0.8%
41
0.8%
51
0.8%
61
0.8%
71
0.8%
81
0.8%
91
0.8%
101
0.8%
111
0.8%
121
0.8%
ValueCountFrequency (%)
1471
0.8%
1461
0.8%
1451
0.8%
1441
0.8%
1431
0.8%
1411
0.8%
1401
0.8%
1391
0.8%
1381
0.8%
1371
0.8%

Happiness
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct123
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.418110283
Minimum3.253560066
Maximum7.476213932
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-05-21T12:26:52.627488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3.253560066
5-th percentile3.9296875
Q14.633840561
median5.552915096
Q36.170477629
95-th percentile7.070834112
Maximum7.476213932
Range4.222653866
Interquartile range (IQR)1.536637068

Descriptive statistics

Standard deviation0.9861343734
Coefficient of variation (CV)0.1820070692
Kurtosis-0.7060558353
Mean5.418110283
Median Absolute Deviation (MAD)0.7672042847
Skewness-0.06580520124
Sum666.4275649
Variance0.9724610023
MonotonicityNot monotonic
2022-05-21T12:26:52.748165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.6395483021
 
0.8%
5.5787429811
 
0.8%
5.7114992141
 
0.8%
6.2012681961
 
0.8%
5.5942702291
 
0.8%
5.7109365461
 
0.8%
5.713295461
 
0.8%
6.5676589011
 
0.8%
4.628132821
 
0.8%
5.8308706281
 
0.8%
Other values (113)113
91.9%
ValueCountFrequency (%)
3.2535600661
0.8%
3.3471212391
0.8%
3.4168629651
0.8%
3.5048811441
0.8%
3.638300181
0.8%
3.7953007221
0.8%
3.9293441771
0.8%
3.9327774051
0.8%
4.0005168911
0.8%
4.0461111071
0.8%
ValueCountFrequency (%)
7.4762139321
0.8%
7.3310360911
0.8%
7.2937278751
0.8%
7.257037641
0.8%
7.225181581
0.8%
7.1032733921
0.8%
7.0743246081
0.8%
7.0394196511
0.8%
6.99175931
0.8%
6.9283475881
0.8%

GDP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct121
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.250361935
Minimum6.623783588
Maximum11.11681843
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-05-21T12:26:52.875453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum6.623783588
5-th percentile7.256841135
Q18.508649826
median9.414566517
Q310.12320328
95-th percentile10.72361774
Maximum11.11681843
Range4.49303484
Interquartile range (IQR)1.614553452

Descriptive statistics

Standard deviation1.105306534
Coefficient of variation (CV)0.1194879229
Kurtosis-0.5875426211
Mean9.250361935
Median Absolute Deviation (MAD)0.8231368065
Skewness-0.5063593532
Sum1137.794518
Variance1.221702533
MonotonicityNot monotonic
2022-05-21T12:26:53.160556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.4145665173
 
2.4%
9.3761453631
 
0.8%
7.9554786681
 
0.8%
10.211576461
 
0.8%
8.9357967381
 
0.8%
9.4121952061
 
0.8%
9.0855712891
 
0.8%
10.010862351
 
0.8%
8.5241107941
 
0.8%
8.5826911931
 
0.8%
Other values (111)111
90.2%
ValueCountFrequency (%)
6.6237835881
0.8%
6.6947269441
0.8%
6.8308744431
0.8%
6.9985480311
0.8%
7.0353589061
0.8%
7.2372751241
0.8%
7.2559013371
0.8%
7.265299321
0.8%
7.3535728451
0.8%
7.4370336531
0.8%
ValueCountFrequency (%)
11.116818431
0.8%
11.090271951
0.8%
10.93408681
0.8%
10.900905611
0.8%
10.800501821
0.8%
10.746841431
0.8%
10.724075321
0.8%
10.719499591
0.8%
10.706581121
0.8%
10.675693511
0.8%

SocialSupport
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct123
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.810296629
Minimum0.5078052282
Maximum0.9667528272
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-05-21T12:26:53.285606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.5078052282
5-th percentile0.6343928456
Q10.7406577766
median0.8286458254
Q30.9001203179
95-th percentile0.931163919
Maximum0.9667528272
Range0.4589475989
Interquartile range (IQR)0.1594625413

Descriptive statistics

Standard deviation0.1021110342
Coefficient of variation (CV)0.1260168567
Kurtosis-0.328431227
Mean0.810296629
Median Absolute Deviation (MAD)0.07451236248
Skewness-0.6985431305
Sum99.66648537
Variance0.01042666331
MonotonicityNot monotonic
2022-05-21T12:26:53.423626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.63769829271
 
0.8%
0.89615130421
 
0.8%
0.89998483661
 
0.8%
0.88185411691
 
0.8%
0.85102856161
 
0.8%
0.83012336491
 
0.8%
0.90204250811
 
0.8%
0.91190481191
 
0.8%
0.82434511181
 
0.8%
0.69026356941
 
0.8%
Other values (113)113
91.9%
ValueCountFrequency (%)
0.50780522821
0.8%
0.55542272331
0.8%
0.58210957051
0.8%
0.59049516921
0.8%
0.60676747561
0.8%
0.6263319851
0.8%
0.63402557371
0.8%
0.63769829271
0.8%
0.63822638991
0.8%
0.64119309191
0.8%
ValueCountFrequency (%)
0.96675282721
0.8%
0.94995784761
0.8%
0.94175457951
0.8%
0.93749529121
0.8%
0.93733179571
0.8%
0.93568634991
0.8%
0.93149459361
0.8%
0.92818784711
0.8%
0.92631661891
0.8%
0.92425078151
0.8%

Health
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct96
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.11859047
Minimum47.29999924
Maximum75.90731812
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-05-21T12:26:53.548052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum47.29999924
5-th percentile52.91000137
Q159.20000076
median65.80000305
Q368.35000229
95-th percentile73.30000305
Maximum75.90731812
Range28.60731888
Interquartile range (IQR)9.150001526

Descriptive statistics

Standard deviation6.499588574
Coefficient of variation (CV)0.1013682385
Kurtosis-0.3519493007
Mean64.11859047
Median Absolute Deviation (MAD)3.500003815
Skewness-0.5953865356
Sum7886.586628
Variance42.24465163
MonotonicityNot monotonic
2022-05-21T12:26:53.668164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66.599998475
 
4.1%
65.800003054
 
3.3%
63.799999243
 
2.4%
673
 
2.4%
68.400001533
 
2.4%
72.199996952
 
1.6%
64.300003052
 
1.6%
68.599998472
 
1.6%
69.800003052
 
1.6%
67.199996952
 
1.6%
Other values (86)95
77.2%
ValueCountFrequency (%)
47.299999241
0.8%
48.900001531
0.8%
49.200000761
0.8%
49.299999241
0.8%
51.200000761
0.8%
51.900001531
0.8%
52.900001531
0.8%
531
0.8%
53.200000761
0.8%
53.299999241
0.8%
ValueCountFrequency (%)
75.907318121
0.8%
74.900001531
0.8%
74.099998471
0.8%
73.599998471
0.8%
73.51
0.8%
73.400001531
0.8%
73.300003052
1.6%
73.099998471
0.8%
731
0.8%
72.699996951
0.8%

Freedom
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct123
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7804558624
Minimum0.4779566526
Maximum0.9637746811
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-05-21T12:26:53.791519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.4779566526
5-th percentile0.5927733958
Q10.716258347
median0.8054486811
Q30.8592651188
95-th percentile0.9228233993
Maximum0.9637746811
Range0.4858180285
Interquartile range (IQR)0.1430067718

Descriptive statistics

Standard deviation0.1066525329
Coefficient of variation (CV)0.1366541505
Kurtosis-0.2736703974
Mean0.7804558624
Median Absolute Deviation (MAD)0.07367947698
Skewness-0.5906312409
Sum95.99607107
Variance0.01137476277
MonotonicityNot monotonic
2022-05-21T12:26:53.921319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.74961102011
 
0.8%
0.73087424041
 
0.8%
0.90506565571
 
0.8%
0.83084261421
 
0.8%
0.92570310831
 
0.8%
0.82655215261
 
0.8%
0.89117145541
 
0.8%
0.89957350491
 
0.8%
0.63161128761
 
0.8%
0.7126570941
 
0.8%
Other values (113)113
91.9%
ValueCountFrequency (%)
0.47795665261
0.8%
0.52744680641
0.8%
0.53811371331
0.8%
0.55282521251
0.8%
0.5637986661
0.8%
0.57034790521
0.8%
0.59250479941
0.8%
0.59519076351
0.8%
0.59887552261
0.8%
0.60455417631
0.8%
ValueCountFrequency (%)
0.96377468111
0.8%
0.96201664211
0.8%
0.9387832881
0.8%
0.93561846021
0.8%
0.92570310831
0.8%
0.92364293341
0.8%
0.92289680241
0.8%
0.92216277121
0.8%
0.92086267471
0.8%
0.91452169421
0.8%

Generosity
Real number (ℝ)

HIGH CORRELATION

Distinct119
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.03246239032
Minimum-0.2534932792
Maximum0.3087729514
Zeros0
Zeros (%)0.0%
Negative77
Negative (%)62.6%
Memory size1.1 KiB
2022-05-21T12:26:54.054089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.2534932792
5-th percentile-0.2260582522
Q1-0.1476506069
median-0.03901394084
Q30.07552246004
95-th percentile0.1983455688
Maximum0.3087729514
Range0.5622662306
Interquartile range (IQR)0.223173067

Descriptive statistics

Standard deviation0.132219683
Coefficient of variation (CV)-4.073011314
Kurtosis-0.5522052047
Mean-0.03246239032
Median Absolute Deviation (MAD)0.1111119799
Skewness0.4225048649
Sum-3.992874009
Variance0.01748204459
MonotonicityNot monotonic
2022-05-21T12:26:54.192821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.039013940845
 
4.1%
-0.032642815261
 
0.8%
-0.082077965141
 
0.8%
-0.14624032381
 
0.8%
-0.16386441891
 
0.8%
0.018430408091
 
0.8%
-0.16012361651
 
0.8%
0.030064493421
 
0.8%
0.1138357521
 
0.8%
-0.017152031881
 
0.8%
Other values (109)109
88.6%
ValueCountFrequency (%)
-0.25349327921
0.8%
-0.24707399311
0.8%
-0.24521587791
0.8%
-0.24293856321
0.8%
-0.23090977971
0.8%
-0.23050585391
0.8%
-0.22703394291
0.8%
-0.21727703511
0.8%
-0.20603477951
0.8%
-0.20424206551
0.8%
ValueCountFrequency (%)
0.30877295141
0.8%
0.28691646461
0.8%
0.24923355881
0.8%
0.24762122331
0.8%
0.24332368371
0.8%
0.20539546011
0.8%
0.19940254091
0.8%
0.18883281951
0.8%
0.18662165111
0.8%
0.16425579791
0.8%

Corruption
Real number (ℝ≥0)

Distinct113
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7776495642
Minimum0.411346525
Maximum0.9367640018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-05-21T12:26:54.320597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.411346525
5-th percentile0.5734172165
Q10.7325151265
median0.7976187766
Q30.851332128
95-th percentile0.9112827003
Maximum0.9367640018
Range0.5254174769
Interquartile range (IQR)0.1188170016

Descriptive statistics

Standard deviation0.1096685773
Coefficient of variation (CV)0.141025704
Kurtosis2.324947436
Mean0.7776495642
Median Absolute Deviation (MAD)0.05807849765
Skewness-1.396317555
Sum95.6508964
Variance0.01202719684
MonotonicityNot monotonic
2022-05-21T12:26:54.452300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.797618776611
 
8.9%
0.8761346341
 
0.8%
0.75568002461
 
0.8%
0.89538413291
 
0.8%
0.80990076071
 
0.8%
0.84077709911
 
0.8%
0.83064633611
 
0.8%
0.71392822271
 
0.8%
0.83489197491
 
0.8%
0.77766001221
 
0.8%
Other values (103)103
83.7%
ValueCountFrequency (%)
0.4113465251
0.8%
0.4140211941
0.8%
0.41581019761
0.8%
0.41861134771
0.8%
0.51830381161
0.8%
0.54304605721
0.8%
0.57161557671
0.8%
0.58963197471
0.8%
0.59161680941
0.8%
0.60148602721
0.8%
ValueCountFrequency (%)
0.93676400181
0.8%
0.92633378511
0.8%
0.92565804721
0.8%
0.92519181971
0.8%
0.92334306241
0.8%
0.92042267321
0.8%
0.91133636241
0.8%
0.91079974171
0.8%
0.91072726251
0.8%
0.89538413291
0.8%

PositiveAffect
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct123
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7007413494
Minimum0.4209618866
Maximum0.9027721286
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-05-21T12:26:54.583315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.4209618866
5-th percentile0.5193038404
Q10.6192112267
median0.7124378681
Q30.7855841517
95-th percentile0.8464069486
Maximum0.9027721286
Range0.4818102419
Interquartile range (IQR)0.166372925

Descriptive statistics

Standard deviation0.1055278937
Coefficient of variation (CV)0.1505946434
Kurtosis-0.5212326334
Mean0.7007413494
Median Absolute Deviation (MAD)0.08259701729
Skewness-0.3202364359
Sum86.19118598
Variance0.01113613634
MonotonicityNot monotonic
2022-05-21T12:26:54.717002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.66924089191
 
0.8%
0.71023023131
 
0.8%
0.64915108681
 
0.8%
0.67743551731
 
0.8%
0.76898145681
 
0.8%
0.7893908621
 
0.8%
0.90277212861
 
0.8%
0.83268880841
 
0.8%
0.59676557781
 
0.8%
0.58616697791
 
0.8%
Other values (113)113
91.9%
ValueCountFrequency (%)
0.42096188661
0.8%
0.44980090861
0.8%
0.45518189671
0.8%
0.50245988371
0.8%
0.50972121951
0.8%
0.51544392111
0.8%
0.51912814381
0.8%
0.52088510991
0.8%
0.53932255511
0.8%
0.54090577361
0.8%
ValueCountFrequency (%)
0.90277212861
0.8%
0.89525455241
0.8%
0.87439584731
0.8%
0.87279176711
0.8%
0.84976416831
0.8%
0.84885060791
0.8%
0.84646707771
0.8%
0.84586578611
0.8%
0.84264230731
0.8%
0.84220093491
0.8%

NegativeAffect
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct123
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2887135525
Minimum0.1141231582
Maximum0.4950400293
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-05-21T12:26:55.014545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.1141231582
5-th percentile0.1759878337
Q10.2316753641
median0.2790243477
Q30.3424236923
95-th percentile0.4254879177
Maximum0.4950400293
Range0.3809168711
Interquartile range (IQR)0.1107483283

Descriptive statistics

Standard deviation0.07741058834
Coefficient of variation (CV)0.2681224614
Kurtosis-0.4908550775
Mean0.2887135525
Median Absolute Deviation (MAD)0.04818814993
Skewness0.3199784186
Sum35.51176696
Variance0.005992399187
MonotonicityNot monotonic
2022-05-21T12:26:55.161808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.333884121
 
0.8%
0.19456052781
 
0.8%
0.29427257181
 
0.8%
0.20338779691
 
0.8%
0.34062150121
 
0.8%
0.39387372141
 
0.8%
0.23178367321
 
0.8%
0.24231933061
 
0.8%
0.41607204081
 
0.8%
0.30834108591
 
0.8%
Other values (113)113
91.9%
ValueCountFrequency (%)
0.11412315821
0.8%
0.14816001061
0.8%
0.16016410291
0.8%
0.16043831411
0.8%
0.16872118411
0.8%
0.17148640751
0.8%
0.17551217971
0.8%
0.18026871981
0.8%
0.18092127141
0.8%
0.18587909641
0.8%
ValueCountFrequency (%)
0.49504002931
0.8%
0.44612428551
0.8%
0.43853390221
0.8%
0.43714874981
0.8%
0.43394353991
0.8%
0.42652237421
0.8%
0.42582425481
0.8%
0.42246088391
0.8%
0.41607204081
0.8%
0.41449379921
0.8%

ConfidenceInGovernment
Real number (ℝ≥0)

HIGH CORRELATION

Distinct111
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4732176158
Minimum0.1109365299
Maximum0.9297930598
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-05-21T12:26:55.316292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.1109365299
5-th percentile0.2227139667
Q10.3338840157
median0.4534069598
Q30.5882932246
95-th percentile0.7939260721
Maximum0.9297930598
Range0.8188565299
Interquartile range (IQR)0.2544092089

Descriptive statistics

Standard deviation0.1832437314
Coefficient of variation (CV)0.3872293111
Kurtosis-0.3625222073
Mean0.4732176158
Median Absolute Deviation (MAD)0.1305845678
Skewness0.4089833269
Sum58.20576674
Variance0.03357826509
MonotonicityNot monotonic
2022-05-21T12:26:55.456278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.453406959813
 
10.6%
0.45773753521
 
0.8%
0.74585360291
 
0.8%
0.50248044731
 
0.8%
0.83773005011
 
0.8%
0.25459533931
 
0.8%
0.38178408151
 
0.8%
0.39099609851
 
0.8%
0.41440093521
 
0.8%
0.55743515491
 
0.8%
Other values (101)101
82.1%
ValueCountFrequency (%)
0.11093652991
0.8%
0.12637971341
0.8%
0.13272963461
0.8%
0.16549026971
0.8%
0.2110006661
0.8%
0.21771809461
0.8%
0.22188259661
0.8%
0.23019629721
0.8%
0.23977972571
0.8%
0.24112360181
0.8%
ValueCountFrequency (%)
0.92979305981
0.8%
0.91333901881
0.8%
0.87664645911
0.8%
0.83993500471
0.8%
0.83927839991
0.8%
0.83773005011
0.8%
0.79649329191
0.8%
0.77082109451
0.8%
0.76835429671
0.8%
0.76658344271
0.8%

Interactions

2022-05-21T12:26:50.625997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:37.614862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:38.759480image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:40.102741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:41.249982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:42.782553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:43.918714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:45.281532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:46.552656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:47.924940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:49.131950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:50.737827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:37.734059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:38.875828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:40.218572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:41.371489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:42.895294image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:44.039574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:45.409011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:46.670765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:48.039537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:49.258653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:50.835929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:37.829716image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:39.153115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:40.315554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:41.476941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:42.987184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:44.143470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:45.512685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:46.770580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:48.139598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:49.375527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:50.932537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:37.925010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:39.259435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:40.416597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:41.588397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:43.098177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:44.240293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:45.627360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:46.877044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:48.246333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:49.514387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:51.032987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:38.024767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:39.364808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:40.515924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:41.716051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:43.215503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:44.331508image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:45.735646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:46.994005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:48.348110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:49.625320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:51.140446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:38.126826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:39.459341image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:40.616719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:42.053957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:43.313143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:44.475896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:45.853720image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:47.103261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:48.454849image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:49.752308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:51.250609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:38.225345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:39.563594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:40.716168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:42.182247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:43.407985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:44.568263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:45.970058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:47.214304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:48.566096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:49.874374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:51.359108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:38.330677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:39.665194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:40.818695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:42.312747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:43.508835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:44.822321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:46.087378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:47.321242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:48.682107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:49.988829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:51.465380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:38.438249image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:39.769862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:40.921193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:42.421022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:43.613574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:44.939988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:46.207566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:47.433714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:48.788366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:50.110236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:51.572585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:38.539311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:39.873439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:41.027950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:42.543305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:43.716079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:45.057638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:46.327481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:47.696979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:48.906628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:50.239640image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:51.700419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:38.659884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:39.999035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:41.152898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:42.666024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:43.824973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:45.180543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:46.447731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:47.826039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:49.029141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T12:26:50.508007image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-05-21T12:26:55.588591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-21T12:26:55.768941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-21T12:26:55.918126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-21T12:26:56.066500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-21T12:26:51.917103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-21T12:26:52.125778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexHappinessGDPSocialSupportHealthFreedomGenerosityCorruptionPositiveAffectNegativeAffectConfidenceInGovernment
024.6395489.3761450.63769868.4000020.749611-0.0326430.8761350.6692410.3338840.457738
146.0393309.8487090.90669968.5999980.831966-0.1826000.8410520.8094230.2917170.305430
254.2877369.0810950.69792566.5999980.613697-0.1339580.8646830.6250140.4371490.246901
367.25703810.7065810.94995873.3000030.9105500.3087730.4113470.7800790.2253610.453407
477.29372810.7240750.90621872.6999970.8900310.1311140.5183040.7475690.1802690.435908
585.1522799.6707620.78703965.1999970.731030-0.2452160.6525390.5923590.1983190.766583
696.22732110.6756940.87574768.5000000.9058590.1281930.7976190.8135710.2897600.453407
7104.3097718.1673470.71255363.7999990.8962170.0162690.6350140.5688270.2135060.876646
8115.5529159.7508000.90025665.8000030.620979-0.1291730.6541130.5409060.2327680.447916
9126.92834810.6609840.92163971.8000030.8568020.0517600.5430460.7863680.2335980.449732

Last rows

df_indexHappinessGDPSocialSupportHealthFreedomGenerosityCorruptionPositiveAffectNegativeAffectConfidenceInGovernment
1131374.3110678.9739080.85832564.3000030.5988760.0171360.9367640.5971120.2347640.110937
1141387.03942011.1168180.83552766.9000020.9620170.1994030.7976190.7950350.2075980.453407
1151397.10327310.5904460.93749572.0999980.8127330.2869160.4186110.7585720.2095720.440121
1161406.99175910.9009060.92100368.4000020.8684970.1888330.6811910.8265550.2682690.386535
1171416.3360109.9306850.91380268.9000020.897852-0.1014150.6265820.8358610.2803230.413032
1181435.0707519.4392960.89587966.3000030.635505-0.2060350.8439690.7256430.3629850.241124
1191445.1752798.7277590.82864667.6999970.805449-0.0390140.7976190.7124380.2790240.453407
1201453.2535609.4145670.78955555.9000020.595191-0.0390140.7976190.4551820.2950640.247787
1211463.9327778.2131790.74375454.7999990.8231690.1289040.7395410.6846230.3871890.717004
1221473.6383007.5494910.75414755.0000000.752826-0.0696700.7512080.8064280.2240510.682647